Spatially explicit structural equation modeling

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.

Tumor-associated mutations inO6-methylguanine DNA-methyltransferase (MGMT) reduce DNA repair functionality

MGMT is the primary vehicle for cellular removal of alkyl lesions from the O-6 position of guanine and the O-4 position of thymine. While key to the maintenance of genomic integrity, MGMT also removes damage induced by alkylating chemotherapies, inhibiting the efficacy of cancer treatment. Germline variants of human MGMT are well-characterized, but somatic variants found in tumors were, prior to this work, uncharacterized. We found that MGMT G132R, from a human esophageal tumor, and MGMT G156C, from a human colorectal cancer cell line, are unable to rescue methyltransferase-deficient Escherichia coli as well as wild type (WT) human MGMT after treatment with a methylating agent. Using pre-steady state kinetics, we biochemically characterized these variants as having a reduced rate constant. G132R binds DNA containing an O6-methylguanine lesion half as tightly as WT MGMT, while G156C has a 40-fold decrease in binding affinity for the same damaged DNA versus WT. Mammalian cells expressing either G132R or G156C are more sensitive to methylating agents than mammalian cells expressing WT MGMT. G132R is slightly resistant to O6-benzylguanine, an inhibitor of MGMT in clinical trials, while G156C is almost completely resistant to this inhibitor. The impared functionality of expressed variants G132R and G156C suggests that the presence of somatic variants of MGMT in a tumor could impact chemotherapeutic outcomes.

Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Predicting the last brand standing in the supermarket price wars

On January 26, 2011, grocery retailer Coles fired the first salvo in what would soon be dubbed the “supermarket price wars” by reducing the price of its own-brand milk to A$1 per litre. Woolworths immediately responded. In the three years since, grocery prices have been tumbling, with 85 cent bread being the latest “sacrificial lamb���. This period of intense competition has brought about not just lower grocery prices, but a senate enquiry, and increasing media and analyst interest.